Data Scientist (Credit) - Car Finance

We started building Marshmallow in 2017. Since then, we’ve grown from 3 to 700+ people, gained unicorn status, raised ~£140M over three funding rounds, turned profitable, insured millions of drivers and lent millions in car loans.

But we’re only just getting started. Our goal is to become one of the largest financial services providers in the world. Over the next 10 years we’ll grow exponentially, not only by scaling our existing products, but also by building new ones.

To achieve our goals we need incredibly ambitious, commercially driven people who never settle for ‘good enough’. Marshmallowers are hungry for autonomy and ownership, and would rather improve than coast. Everyone raises standards and has an impact, with a focus on collective success over self-interest.

We’ve created an environment where curious, tenacious people win and grow together. If that sounds motivating, this could be the place for you.

We are not able to offer visa sponsorship for this position.

Marshmallow is opening up access to credit to migrants, bringing a brand new product to market which provides newcomers to the UK with fairly priced car finance for the first time.

Our Car Finance team is building a new generation lending platform from the ground up, making it simple, transparent and accessible for people who need finance to get on the road. We work closely with dealers, brokers and customers to make sure that every application, decision and interaction is fast, fair and easy to understand.

Because we’re a small, high-performing team, everyone has real ownership. We combine hands-on operations with a strong focus on automation and technology. We’re designing highly automated, AI-powered processes that let us move faster and smarter than traditional lenders.

The Role

We're hiring a Data Scientist to sit at the heart of how we lend: who we say yes to, at what price, and how we know it's working.

Our customers are people that traditional lenders struggle to assess. Many are new to the UK, have thin or no credit files, and don't fit neatly into off-the-shelf bureau scores. That makes credit decisioning at Marshmallow a genuinely hard, genuinely interesting problem: we combine bureau data, Open Banking, insurance, application and behavioural data to build a view of risk that others can't, and we need to do it in a way that is fair, explainable and compliant.

This role sits between a traditional credit analyst and a data scientist. You'll build and monitor scorecards, design and test credit strategies, dig into portfolio performance to find out what's really going on, and turn large, messy datasets into insights that change how we lend. You'll own your work end-to-end: from framing the question and pulling the data through to modelling, presenting a recommendation and seeing it live in our decision engine.

You'll be working with a small team and a modern stack, alongside a Head of Credit who has built this function from scratch and who will give you real ownership from day one. This is not a role where you hand models over a wall and move on - you'll see the impact of your decisions immediately.

What you'll be doing

• Build, validate and monitor application and behavioural scorecards, using bureau, Open Banking and internal data to improve how we assess new-to-UK and thin-file customers.

• Design, test and refine our credit strategy: cut-offs, affordability rules, pricing tiers and limits, with a clear view of the trade-off between risk, volume and margin.

• Run experiments and champion/challenge tests to prove that changes to policy and models actually work before we scale them.

• Explore new data sources and alternative signals, and build the case for using (or not using) them in decisioning.

• Support model governance: document what you build, evidence that it treats customers fairly, and help us meet our Consumer Duty, CONC and model risk obligations.

• Work with our engineers to get models and rules into production, and use AI tooling to make yourself and the team faster.

What You'll Bring

Must have

• 2–5 years' experience in decision science or data science within consumer lending or another regulated financial services environment.

• Hands-on experience developing or materially refining credit scorecards or risk models (logistic regression and/or gradient boosting), including the practicalities: sample design, binning, reject inference, validation and monitoring.

• Experience working with Open Banking or other alternative data in credit decisioning.

• SQL user, and comfortable working with large, imperfect datasets.

• A solid grounding in statistics and a good instinct for when a result is real and when it isn't.

• Commercial judgement: you can translate a model output into a lending decision and explain the trade-offs to a non-technical audience.

• Curiosity and ownership. You ask why, you dig until you find the answer, and you'd rather ship something and improve it than wait for perfection.

Nice to have

• Motor finance or other secured lending experience.

• Experience of working within a rapidly scaling lending environment.

• Familiarity with UK consumer credit regulation and how it shapes model design and use.

• Experience using LLMs to speed up analytical work.

• You already know some of the tooling Marshmallow uses: Snowflake, Python, Claude, Linear, Notion

Perks of the job

• Bonus scheme designed to reward high performance

• Private medical insurance with Vitality, mental health support with Oliva

• Personal learning budget and 2 dedicated L&D days a year

• Monthly flexible benefits budget to spend as you choose

• 25 days holiday plus bank holidays

• 4 weeks Work From Anywhere per year

Our process

• Initial call with a member from our Talent Team (30 mins)

• Past Experience interview with Hiring Manager (60 mins)

• Onsite interview consisting of:

  • Technical interview with a couple of the team (60 mins)

  • Culture interview (60 mins)

Diversity of thought

We know the best ideas come from having different perspectives in the room - and we're committed to hiring fairly, regardless of background, identity or experience. If you see yourself in this role, we'd encourage you to apply.